A Diffusion-Neuron Hybrid System for Molecular Communication

TL;DR

This paper introduces a hybrid molecular communication system combining diffusion-based and neural channels, utilizing connection nano-devices to enable information exchange, and demonstrates how parameter optimization can enhance communication reliability.

Contribution

It proposes a novel hybrid communication system integrating diffusion and neural channels with a mechanism for information exchange via connection nano-devices, supported by a mathematical model and simulations.

Findings

The hybrid system effectively combines two communication modalities.

Parameter optimization improves bit error rate performance.

Simulation results confirm the system's reliability.

Abstract

Diffusion-based and neural communication are two interesting domains in molecular communication. Both of them have distinct advantages and are exploited separately in many works. However, in some cases, neural and diffusion-based ways have to work together for a communication. Therefore, in this paper, we propose a hybrid communication system, in which the diffusion-based and neural communication channels are contained. Multiple connection nano-devices (CND) are used to connect the two channels. We define the practice function of the CNDs and develop the mechanism of exchanging information from diffusion-based to neural channel, based on the biological characteristics of the both channels. In addition, we establish a brief mathematical model to present the complete communication process of the hybrid system. The information exchange process at the CNDs is shown in the simulation. The bit error rate (BER) indicator is used to verify the reliability of communication. The result reveals that based on the biological channels, optimizing some parameters of nano-devices could improve the reliability performance.